Faculty Publications
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Item Inverse Estimation of Interfacial Heat Transfer Coefficient During the Solidification of Sn-5wt%Pb Alloy Using Evolutionary Algorithm(Pleiades journals, 2019) Vishweshwara, P.S.; Gnanasekaran, N.; Mahalingam, M.The study of the interfacial heat transfer coefficient (IHTC) is one of the major concerns during solidification of casting. In order to find out the IHTC at the metal–mold interface, a one dimensional transient heat conduction model is numerically investigated during horizontal directional solidification of Sn–5wt%Pb alloy. The forward model is solved using explicit finite difference method to obtain the exact temperatures for the known boundary conditions. The estimation of the unknown IHTC is attempted using Particle Swarm Optimization (PSO) as an inverse approach along with Bayesian framework. In order to prove the robustness of the proposed methodology, the estimation is accomplished for the simulated measurements. The simulated measurements are then added with noise to replicate the experimental data. The present approach not only minimizes the difference between simulated and measured temperatures but also takes in to account “a priori” information about the unknown parameters. © 2019, Springer Nature Singapore Pte Ltd.Item Parameter estimation using heat transfer models with experimental data using a combined ann-Bayesian approach(Begell House Inc., 2014) Gnanasekaran, N.; Shankar, N.T.; Balaji, C.A hybrid approach, wherein Markov Chain Monte Carlo simulations are used in a Bayesian framework, in conjunction with artificial neural networks (ANN) is developed for solving an inverse heat conduction problem. Steady state three-dimensional heat conduction from a Teflon cylinder with uniform volumetric internal heat generation is considered. The goal is to estimate qv, given the heat transfer coefficient h, the thermal conductivity k and temperature data at certain fixed locations on the surface of the cylinder. For the purposes of establishing the soundness and efficacy of the approach, temperatures obtained by a numerical solution to the governing equation for known values of the parameter qv are first used to retrieve the quantities of interest, followed by retrievals with actual measurements. In order to significantly reduce the computational time associated with the MCMC simulations, first, a neural network is trained with limited number of solutions to the forward model. This serves as a surrogate to replace the forward model (conduction equation) during the process of retrievals with Markov Chain Monte Carlo simulations in a Bayesian framework. The performance of the proposed hybrid technique is evaluated for different cases.Item MCMC and approximation error model for the simultaneous estimation of heat flux and heat transfer coefficient using heat transfer experiments(Begell House Inc., 2018) Gnanasekaran, N.; Kumar, M.K.; Balaji, C.This work deals with the simultaneous estimation of the heat flux and the heat transfer coefficient from a mild steel fin losing heat to the ambient by natural convection. Steady state heat transfer experiments are performed on a mild steel fin of dimension 150x250x6 (all dimensions are in mm) placed on to an aluminum base plate of dimension 150x250x8 (all dimensions are in mm). The experimental set up is placed inside a large enclosure to avoid natural disturbances. Nine calibrated K-type thermocouples are used to measure the temperatures of the fin and the base plate. The forward solution of a three dimensional conjugate heat transfer fin model is solved using commercially available ANSYS software in order to obtain the temperature distribution of the fin. An inverse problem is proposed for the estimation of unknown parameters within the Bayesian framework of statistics. Furthermore, a model reduction in the form of Approximation Error Model (AEM) is considered for the inverse conjugate natural convection heat transfer problem. Such an approach not only mitigates the complexity of the inverse problem but also compensates the model reduction with all necessary statistical parameters. Additionally, the sample space within the Bayesian framework is explored with the help of Markov Chain Monte Carlo Method (MCMC) along with the Metropolis-Hastings algorithm. The results of the inverse estimation using Approximation Error Model based on the experimental temperature prove to be a promising alternative in inverse conjugate heat transfer problems. © 2018 International Heat Transfer Conference. All rights reserved.Item A Surrogate Forward Model Using Artificial Neural Networks in Conjunction with Bayesian Computations for 3D Conduction-Convection Heat Transfer Problem(Springer, 2020) Kumar, M.K.; Vishweshwara, P.S.; Gnanasekaran, N.The present work describes the determination of heat flux at the boundary for a conjugate heat transfer problem based on a coupled three-dimensional conduction-convection fin numerical model, also referred to as complete model. The model is developed using commercially available software and solved along with Navier–Stokes equation in order to acquire the required temperature distribution. An inverse analysis is proposed by treating the boundary heat flux as unknown while the temperatures of the fin are known. The inverse analysis is greatly accomplished with the help of Bayesian framework that combines the solution of the forward model and the simulated measurements. Markov chain Monte Carlo (MCMC) is applied to explore the sample space that drives samples to proper convergence and the selection or acceptance of the new samples is performed using Metropolis–Hastings algorithm. Thus, the novelty of the present work is the use of artificial neural network (ANN) as surrogate model, that not only retains the full nature of the complete model but also acts as a fast forward model in the inverse analysis, within the Bayesian framework that quantifies the uncertainty of heat flux. The results of the present work emphasize that even for noise-added temperature data the final estimates are very close to the actual values and the uncertainty of the unknown heat flux is reported in terms of standard deviation accompanied by mean and maximum a posteriori (MAP). © 2020, Springer Nature Singapore Pte Ltd.Item Inverse approach for estimating boundary properties in a transient fin problem(Springer, 2018) Gnanasekaran, N.; Balaji, S.A solution methodology is proposed for an inverse estimation of boundary conditions from the knowledge of transient temperature data. A forward model based on prevalent time-dependent heat conduction fin equation is solved using a fully implicit finite volume method. First, the inverse model is formulated and accomplished for time-invariant heat flux at the fin base, and later extended to transient heat flux, base temperature and average heat transfer coefficient. Secondly, the Nusselt number is then replaced with Rayleigh number in the forward model to realistically estimate the base temperature, which varies with respect to time, based on in-house transient fin heat transfer experiments. This scenario further corroborates the validation of the proposed inverse approach. The experimental set-up consists of a mild steel 250×150×6mm3 fin mounted centrally on an aluminium base 250×150×8mm3 plate. The base is attached to an electrical heater and insulated with glass-wool to prevent heat loss to surroundings. Five calibrated K-type thermocouples are used to measure temperature along the fin. The functional form of the unknown parameters is not known beforehand; sensitivity studies are performed to determine suitability of the estimation and location of sensors for the inverse approach. Conjugate gradient method with adjoint equation is chosen as the inverse technique and the study is performed as a numerical optimization; subsequently, the estimates show satisfactory results. © 2018, Indian Academy of Sciences.Item A combined ANN-GA and experimental based technique for the estimation of the unknown heat flux for a conjugate heat transfer problem(Springer Verlag service@springer.de, 2018) Kumar, M.K.; Vishweshwara, P.S.; Gnanasekaran, N.; Balaji, C.The major objectives in the design of thermal systems are obtaining the information about thermophysical, transport and boundary properties. The main purpose of this paper is to estimate the unknown heat flux at the surface of a solid body. A constant area mild steel fin is considered and the base is subjected to constant heat flux. During heating, natural convection heat transfer occurs from the fin to ambient. The direct solution, which is the forward problem, is developed as a conjugate heat transfer problem from the fin and the steady state temperature distribution is recorded for any assumed heat flux. In order to model the natural convection heat transfer from the fin, an extended domain is created near the fin geometry and air is specified as a fluid medium and Navier Stokes equation is solved by incorporating the Boussinesq approximation. The computational time involved in executing the forward model is then reduced by developing a neural network (NN) between heat flux values and temperatures based on back propagation algorithm. The conjugate heat transfer NN model is now coupled with Genetic algorithm (GA) for the solution of the inverse problem. Initially, GA is applied to the pure surrogate data, the results are then used as input to the Levenberg- Marquardt method and such hybridization is proven to result in accurate estimation of the unknown heat flux. The hybrid method is then applied for the experimental temperature to estimate the unknown heat flux. A satisfactory agreement between the estimated and actual heat flux is achieved by incorporating the hybrid method. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.Item 3D coupled conduction-convection problem using in-house heat transfer experiments in conjunction with hybrid inverse approach(Emerald Group Holdings Ltd., 2019) Vishweshwara, P.S.; Kumar, M.K.; Gnanasekaran, N.; Mahalingam, A.Purpose: Many a times, the information about the boundary heat flux is obtained only through inverse approach by locating the thermocouple or temperature sensor in accessible boundary. Most of the work reported in literature for the estimation of unknown parameters is based on heat conduction model. Inverse approach using conjugate heat transfer is found inadequate in literature. Therefore, the purpose of the paper is to develop a 3D conjugate heat transfer model without model reduction for the estimation of heat flux and heat transfer coefficient from the measured temperatures. Design/methodology/approach: A 3 D conjugate fin heat transfer model is solved using commercial software for the known boundary conditions. Navier–Stokes equation is solved to obtain the necessary temperature distribution of the fin. Later, the complete model is replaced with neural network to expedite the computations of the forward problem. For the inverse approach, genetic algorithm (GA) and particle swarm optimization (PSO) are applied to estimate the unknown parameters. Eventually, a hybrid algorithm is proposed by combining PSO with Broyden–Fletcher–Goldfarb–Shanno (BFGS) method that outperforms GA and PSO. Findings: The authors demonstrate that the evolutionary algorithms can be used to obtain accurate results from simulated measurements. Efficacy of the hybrid algorithm is established using real time measurements. The hybrid algorithm (PSO-BFGS) is more efficient in the estimation of unknown parameters for experimentally measured temperature data compared to GA and PSO algorithms. Originality/value: Surrogate model using ANN based on computational fluid dynamics simulations and in-house steady state fin experiments to estimate the heat flux and heat transfer coefficient separately using GA, PSO and PSO-BFGS. © 2019, Emerald Publishing Limited.